Life cycle assessment of an automated multi-cellular case-picking system

Abstract

This study evaluated the environmental impact of an automated multi-cellular case-picking system using a Life Cycle Assessment methodology with the aim of improving logistics efficiency and sustainability. The system integrates advanced technologies, such as laser-guided vehicles, automated storage and retrieval systems, robotized layer-picking and case-picking units, and wrapping units, to create multi-product palletized unit loads for the food and beverage sector. A key contribution of this study is the data-driven approach developed to analyze this complex system comprising thousands of parts. Primary data were gathered from bills of material and onsite energy monitoring, whereas analytical and simulation models provided accurate estimations of energy use across handling activities. Life-cycle impact assessments focus on climate change, specifically its effects on human health and the ecosystem. This study underscores the critical role of high-quality data in environmental assessments and offers insights for advancing sustainable logistics and material handling practices. The proposed methodology is scalable and offers insights into other industrial applications, with a different number and type of robotized cells, working cycles, and material handling vehicles. • Evaluate environmental impact of automated case-picking system via LCA. • Quantify energy use using primary data and advanced analytical/simulation models. • Focus on climate change effects on human health and ecosystems in logistics. • Propose a scalable methodology for diverse robotic and material handling systems. • First study on sustainable design in complex automated material handling systems.